TensorFlow DNNRegressor 从训练回归器中检索损失值

Posted 2023-03-28

【中文标题】TensorFlow DNNRegressor 从训练回归器中检索损失值

【英文标题】：TensorFlow DNNRegressor Retrieve Loss Values from Training Regressor

【发布时间】：2018-05-14 17:55:46

【问题描述】：

我想在训练 tensorflow.estimator.DNNRegressor 模型时捕获日志中输出的每个步骤的损失值。

给出一个琐碎的废话示例：

In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: import tensorflow as tf
In [4]: X = pd.DataFrame('X1':np.arange(1,10), 'X2':np.arange(11,20))
In [5]: X
Out[5]:
   X1  X2
0   1  11
1   2  12
2   3  13
3   4  14
4   5  15
5   6  16
6   7  17
7   8  18
8   9  19

In [6]: y = pd.Series(np.arange(1,10)/2)
In [7]: feature_cols = [tf.feature_column.numeric_column(col)
                       for col in X.columns]

In [8]: regressor = 
tf.estimator.DNNRegressor(feature_columns=feature_cols,
                          hidden_units=[3,3],
                          model_dir='mymodel')
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: '_tf_random_seed': 1, 
'_save_checkpoints_secs': 600, '_log_step_count_steps': 100, 
'_keep_checkpoint_max': 5, '_save_checkpoints_steps': None, 
'_session_config': None, '_model_dir': 'mymodel', 
'_keep_checkpoint_every_n_hours': 10000, '_save_summary_steps': 100

In [9]: regressor.train(
          input_fn=tf.estimator.inputs.pandas_input_fn(
                                    x=X,
                                    y=y,
                                    num_epochs=None,
                                    shuffle=True), 
                                    steps=1000)
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Saving checkpoints for 1 into mymodel/model.ckpt.
INFO:tensorflow:step = 1, loss = 1090.0
INFO:tensorflow:global_step/sec: 685.57
INFO:tensorflow:step = 101, loss = 580.524 (0.146 sec)
INFO:tensorflow:global_step/sec: 807.454
INFO:tensorflow:step = 201, loss = 423.964 (0.124 sec)
INFO:tensorflow:global_step/sec: 875.857
INFO:tensorflow:step = 301, loss = 353.421 (0.114 sec)
INFO:tensorflow:global_step/sec: 788.649
INFO:tensorflow:step = 401, loss = 297.249 (0.127 sec)
INFO:tensorflow:global_step/sec: 649.258
INFO:tensorflow:step = 501, loss = 254.237 (0.154 sec)
INFO:tensorflow:global_step/sec: 803.059
INFO:tensorflow:step = 601, loss = 303.544 (0.125 sec)
INFO:tensorflow:global_step/sec: 674.359
INFO:tensorflow:step = 701, loss = 234.27 (0.148 sec)
INFO:tensorflow:global_step/sec: 818.35
INFO:tensorflow:step = 801, loss = 259.353 (0.122 sec)
INFO:tensorflow:global_step/sec: 672.83
INFO:tensorflow:step = 901, loss = 208.319 (0.149 sec)
INFO:tensorflow:Saving checkpoints for 1000 into mymodel/model.ckpt.
INFO:tensorflow:Loss for final step: 200.45.
Out[9]: <tensorflow.python.estimator.canned.dnn.DNNRegressor at 0x1076d5470>

因此，在上面的输出中，我想捕获 step = N 和 loss = value，以便我可以绘制并进一步分析它。

在这里感谢任何帮助

【参考方案1】：

好的，所以我不一定想出一种方法来检索训练损失值，但是我确实找到了一种方法来捕获和绘制从 evaluate() 函数返回的损失函数值，这几乎可以让我得到信息我需要确定我是否过度拟合了我的数据。

与我上面的小例子保持一致，它是这样的。

In [1]: import tensorflow as tf
In [2]: from sklearn.model_selection import train_test_split
In [3]: import pandas as pd
In [4]: import numpy as np
In [5]: X = pd.DataFrame('X1':np.arange(1,10), 'X2':np.arange(11,20))
In [6]: y = pd.Series(np.arange(1,10)/2)
In [7]: X_train, X_validate, y_train, y_validate = train_test_split(X, y, test_size=0.2, random_state=23)
In [8]: feature_cols = [tf.feature_column.numeric_column(col)
...:                 for col in X.columns]
...:
In [9]: regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
...:                                     hidden_units=[3,3],
...:                                     model_dir='fake_model')
In [10]: validation_losses = []
In [11]: for _ in range(10):
...:      regressor.train(input_fn=tf.estimator.inputs.pandas_input_fn(
                   x=X_train, 
                   y=y_train,
                   num_epochs=None,
                   shuffle=True), steps=10)
...:     validation_losses.append(regressor.evaluate(
             input_fn=tf.estimator.inputs.pandas_input_fn(
                 x=X_validate,
                 y=y_validate,
                 num_epochs=1,
                 shuffle=False)))
In [12]: import matplotlib.pyplot as plt
In [13]: losses = [l['loss'] for l in validation_losses]
In [14]: steps = [s for s in range(10)]
In [15]: plt.scatter(x=steps, y=losses)
Out[15]: <matplotlib.collections.PathCollection at 0x1114e7048>
In [16]: plt.show()